{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"Provides AI models for table structure recognition and page layout detection to support PDF document conversion.","skillfed_tags":["document-processing","table-extraction","layout-detection"],"use_cases":["Extract table structure and cell locations from scanned PDF pages for downstream OCR or data extraction.","Detect and segment page layout regions to guide document parsing and conversion.","Batch process document images to identify which pages contain tables before specialized recognition.","Build a document conversion pipeline that preserves table formatting when converting PDFs to structured formats.","Analyze financial or scientific documents where accurate table extraction is critical for data integrity."],"what_it_does":"This package provides pre-trained neural networks for understanding document structure, with two main components: TableFormer, which identifies table boundaries and cell structure in document images, and a Layout model that detects tables and other page elements. It wraps transformer-based models trained on datasets including PubTabNet, FinTabNet, and TableBank, designed to integrate with document conversion pipelines.\n\nThe package depends on torch, transformers, and huggingface_hub to load and run inference. You install it with an optional choice between opencv-python and opencv-python-headless, then instantiate predictors and call them on image data. Models are downloaded from huggingface_hub on first use.","worth_installing":"Yes, if you are building a document processing pipeline that needs table and layout detection. The package is actively maintained, has no known vulnerabilities, and integrates with the broader ecosystem. The MIT license poses no barrier. Install friction is low, though torch and transformers are substantial dependencies\u2014acceptable for ML workloads but not for lightweight applications. Suitable for production use."},"id":"docling-ibm-models","links":{"html":"https://skillfed.io/packages/docling-ibm-models","md":"https://skillfed.io/packages/docling-ibm-models.md","pypi":"https://pypi.org/project/docling-ibm-models/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-11","license_spdx":"MIT","license_treatment":"permissive","name":"docling-ibm-models","python_support":"supports_current","summary":"This package contains the AI models used by the Docling PDF conversion package"},"popularity":{"monthly_downloads":3514585,"position":2595,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"3.14.0"}